Exponential random graphs behave like mixtures of stochastic block models

TL;DR

This paper demonstrates that exponential random graphs can be approximated as mixtures of stochastic block models, with behavior depending on the graph density, and extends previous results to sparser regimes with stronger bounds.

Contribution

It establishes that exponential random graphs are approximate mixtures of block models and proves existence and uniqueness of solutions in various regimes, extending prior work.

Findings

Exponential random graphs behave like mixtures of stochastic block models in dense regimes.

Solutions to the associated matrix equation are close to block matrices.

The results extend to sparse regimes with improved bounds.

Abstract

We study the behavior of exponential random graphs in both the sparse and the dense regime. We show that exponential random graphs are approximate mixtures of graphs with independent edges whose probability matrices are critical points of an associated functional, thereby satisfying a certain matrix equation. In the dense regime, every solution to this equation is close to a block matrix, concluding that the exponential random graph behaves roughly like a mixture of stochastic block models. We also show existence and uniqueness of solutions to this equation for several families of exponential random graphs, including the case where the subgraphs are counted with positive weights and the case where all weights are small in absolute value. In particular, this generalizes some of the results in a paper by Chatterjee and Diaconis from the dense regime to the sparse regime and strengthens their bounds from the cut-metric to the one-metric.